Overview
WinoGrande is a large-scale commonsense reasoning dataset from AI2 testing pronoun resolution requiring world knowledge. Built at 44,000 problems using crowdsourcing with adversarial filtering, it provides more robust evaluation than the original Winograd Schema Challenge.
What’s In It
WinoGrande contains 44,000 fill-in-the-blank problems where the correct answer requires commonsense reasoning about physical properties, social interactions, or everyday knowledge. Adversarial filtering ensures problems cannot be solved by statistical shortcuts.
How It’s Used
WinoGrande is part of standard language model evaluation suites including the Open LLM Leaderboard. It tests whether models possess implicit world knowledge necessary for natural language understanding.
Controversies
Despite adversarial filtering, some problems may contain spurious statistical cues. The binary-choice format limits assessment to recognition rather than generation of commonsense knowledge.